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http://dx.doi.org/10.5370/JEET.2010.5.1.070

Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement  

Li, Zhihuan (Dept. of Electrical and Electronic Engineering, Huazhong Univ. of Sci. and Tech.)
Li, Yinhong (Dept. of Electrical and Electronic Engineering, Huazhong Univ. of Sci. and Tech.)
Duan, Xianzhong (Dept. of Electrical and Electronic Engineering, Huazhong Univ. of Sci. and Tech.)
Publication Information
Journal of Electrical Engineering and Technology / v.5, no.1, 2010 , pp. 70-78 More about this Journal
Abstract
Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.
Keywords
Optimal reactive power flow; Elitist nondominated sorting genetic algorithm; Pareto optimal; Multiobjective evolutionary algorithms;
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